






"""simple docstring"""





import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
_SCREAMING_SNAKE_CASE   :							Any          =		logging.getLogger(__name__)







def 	lowerCamelCase__			(				) ->							Dict:
  lowerCamelCase_       	=		argparse.ArgumentParser(
      description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'					)
  parser.add_argument('--file_path'						,							type=_lowerCamelCase						,							default='data/dump.txt'						,							help='The path to the data.'					)
  parser.add_argument('--tokenizer_type'						,							type=_lowerCamelCase						,							default='bert'						,							choices=['bert', 'roberta', 'gpt2']					)
  parser.add_argument('--tokenizer_name'						,							type=_lowerCamelCase						,							default='bert-base-uncased'						,							help='The tokenizer to use.'					)
  parser.add_argument('--dump_file'						,							type=_lowerCamelCase						,							default='data/dump'						,							help='The dump file prefix.'					)
  lowerCamelCase_       	=		parser.parse_args()

  logger.info(F'''Loading Tokenizer ({args.tokenizer_name})'''					)
  if args.tokenizer_type == "bert":
    lowerCamelCase_       	=		BertTokenizer.from_pretrained(args.tokenizer_name					)
    lowerCamelCase_       	=		tokenizer.special_tokens_map['cls_token']  # `[CLS]`
    lowerCamelCase_       	=		tokenizer.special_tokens_map['sep_token']  # `[SEP]`
  elif args.tokenizer_type == "roberta":
    lowerCamelCase_       	=		RobertaTokenizer.from_pretrained(args.tokenizer_name					)
    lowerCamelCase_       	=		tokenizer.special_tokens_map['cls_token']  # `<s>`
    lowerCamelCase_       	=		tokenizer.special_tokens_map['sep_token']  # `</s>`
  elif args.tokenizer_type == "gpt2":
    lowerCamelCase_       	=		GPTaTokenizer.from_pretrained(args.tokenizer_name					)
    lowerCamelCase_       	=		tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
    lowerCamelCase_       	=		tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

  logger.info(F'''Loading text from {args.file_path}'''					)
  with open(args.file_path						,							'r'						,							encoding='utf8'					) as fp:
    lowerCamelCase_       	=		fp.readlines()

  logger.info('Start encoding'					)
  logger.info(F'''{len(_lowerCamelCase					)} examples to process.'''					)

  lowerCamelCase_       	=		[]
  lowerCamelCase_       	=		0
  lowerCamelCase_       	=		10000
  lowerCamelCase_       	=		time.time()
  for text in data:
    lowerCamelCase_       	=		F'''{bos} {text.strip()} {sep}'''
    lowerCamelCase_       	=		tokenizer.encode(_lowerCamelCase						,							add_special_tokens=_lowerCamelCase					)
    rslt.append(_lowerCamelCase					)

    iter += 1
    if iter % interval == 0:
      lowerCamelCase_       	=		time.time()
      logger.info(F'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''					)
      lowerCamelCase_       	=		time.time()
  logger.info('Finished binarization'					)
  logger.info(F'''{len(_lowerCamelCase					)} examples processed.'''					)

  lowerCamelCase_       	=		F'''{args.dump_file}.{args.tokenizer_name}.pickle'''
  lowerCamelCase_       	=		tokenizer.vocab_size
  if vocab_size < (1 << 16):
    lowerCamelCase_       	=		[np.uintaa(_lowerCamelCase					) for d in rslt]
  else:
    lowerCamelCase_       	=		[np.intaa(_lowerCamelCase					) for d in rslt]
  random.shuffle(rslt_					)
  logger.info(F'''Dump to {dp_file}'''					)
  with open(_lowerCamelCase						,							'wb'					) as handle:
    pickle.dump(rslt_						,							_lowerCamelCase						,							protocol=pickle.HIGHEST_PROTOCOL					)


if __name__ == "__main__":
  main()












"""simple docstring"""





import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_SCREAMING_SNAKE_CASE   :							int          =		pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		dataset.iloc[:, 1:2].values
_SCREAMING_SNAKE_CASE   :							str          =		dataset.iloc[:, 2].values


_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		train_test_split(X, y, test_size=0.2, random_state=0)


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		PolynomialFeatures(degree=4)
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		poly_reg.fit_transform(X)
_SCREAMING_SNAKE_CASE   :							List[str]          =		LinearRegression()
pol_reg.fit(X_poly, y)







def 	lowerCamelCase__			(				) ->							List[str]:
  plt.scatter(_lowerCamelCase						,							_lowerCamelCase						,							color='red'					)
  plt.plot(_lowerCamelCase						,							pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase					)					)						,							color='blue'					)
  plt.title('Truth or Bluff (Linear Regression)'					)
  plt.xlabel('Position level'					)
  plt.ylabel('Salary'					)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003




